A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

• A fast, simple method to render sky color using gradients maps [[Abad06]]
• A Framework for the Experimental Comparison of Solar and Skydome Illumination [[Kider14]]
• A Method for Modeling Clouds based on Atmospheric Fluid Dynamics [[Miyazaki01]]
• A Physically-Based Night Sky Model [[Jensen01]]

Some notes and tools for reverse engineering / deobfuscating / unminifying obfuscated web app code.

• tl;dr: AKA: devalias's shortlist
• PoC
• Benchmarks / Leaderboards / etc

name: coach-repo-bootstrap description: >- Interview a user and scaffold a personalized git-based repo where an LLM acts as their ongoing strength-and-conditioning coach, with all state kept in files rather than the model's context. Use this whenever someone wants to set up a workout or training tracker, a personal-coach or AI-fitness-log repo, an RPE-autoregulated strength program, or any long-running per-session tracking system an agent maintains across sessions — even if they never say "skill," "scaffold," or "repo," and even if they only describe the outcome ("I want an AI to plan and log my workouts and handle the progression for me").